How did we get here? Milestones in the journey to generative AI Machine learning: Analysis and prediction phase The first decade of the 2000s marked the rapid advance of various machine learning techniques that could analyze massive amounts of online data to draw conclusions – or “learn” – from the results. Since then, companies have viewed machine learning as an incredibly powerful field of AI for analyzing data, finding patterns, generating insights, making predictions and automating tasks at a pace and on a scale that was previously impossible. Deep learning: Vision and speech phase The 2010s produced advances in AI’s perception capabilities in the field of machine learning called deep learning. Breakthroughs in deep learning enable the computer vision that search engines and self-driving cars use to classify and detect objects, as well as the voice recognition that allows popular AI speech assistants to respond to users in a natural way. Generative AI: Enter the language-mastery phase Building on exponential increases in the size and capabilities of deep learning models, the 2020s will be about language mastery. The GPT-4 language model, developed by OpenAI, marks the beginning of a new phase in the abilities of language-based AI applications. Models such as this will have far-reaching consequences for business, since language permeates everything an organization does day to day—its institutional knowledge, communication and processes. 2 4 A new era of generative AI for everyone |